About this Course

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100% online
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Intermediate Level

Calculus, Linear algebra, Python, NumPy, Pandas, Matplotlib, and Scikit-learn. Familiarity with classic Supervised and Unsupervised Learning.

Approx. 60 hours to complete
English

What you will learn

  • Apply different optimization methods while training and explain different behavior.

  • Use cloud tools and deep learning libraries to implement CNN architecture and train for image classification tasks.

  • Apply deep learning package to sequential data, build models, train, and tune.

Skills you will gain

  • Deep Learning
  • Artificial Neural Network
  • Convolutional Neural Network
  • Unsupervised Deep Learning
  • Recurrent Neural Network
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level

Calculus, Linear algebra, Python, NumPy, Pandas, Matplotlib, and Scikit-learn. Familiarity with classic Supervised and Unsupervised Learning.

Approx. 60 hours to complete
English

Offered by

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University of Colorado Boulder

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Data Science from University of Colorado Boulder. If you are admitted to the full program, your courses count towards your degree learning.

Syllabus - What you will learn from this course

Week
1
Week 1
10 hours to complete

Deep Learning Introduction, Multilayer Perceptron

10 hours to complete
6 videos (Total 68 min), 8 readings, 6 quizzes
Week
2
Week 2
8 hours to complete

Training Neural Networks

8 hours to complete
6 videos (Total 86 min), 1 reading, 2 quizzes
Week
3
Week 3
15 hours to complete

Deep Learning on Images

15 hours to complete
11 videos (Total 135 min), 1 reading, 2 quizzes
Week
4
Week 4
14 hours to complete

Deep Learning on Sequential Data

14 hours to complete
4 videos (Total 44 min), 2 readings, 1 quiz

About the Machine Learning: Theory and Hands-on Practice with Python Specialization

Machine Learning: Theory and Hands-on Practice with Python

Frequently Asked Questions

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